Eli Pariser on Machine Curation and Paying Attention to the Things We Don't Know

Eli Pariser spent the last decade organizing people. Now, the former executive director of MoveOn.org, is organizing information.

Algorithms generally manage the enormous amounts of available data by giving you more of what you like with every click. The trouble, says Pariser, is that the vision of perfect machine categorization often fails to adequately represent the multiple ways that information can be grouped.

To underscore his point, Pariser tells the story about physicist Niels Bohr, who once proposed calculating the height of a building by using a barometer as a weight. Bohr’s failure to use the barometer as a measurement device meant he failed his high school physics exam but it also reveals a flexibility of thought that machine curation does not adequately capture.

For example, Netflix bases its categorization system on prediction, namely “if you like this, you’ll like that.” Once the Netflix model determines a user like romantic comedies, says Pariser, it will continue to suggest romantic comedies, effectively narrowing the types recommended. In other words, the Netflix algorithm doesn’t provide users with risky choices.

Pariser says that machine curation also risks reducing the “noise,” namely the randomness and unpredictably, of information to a point that it impedes the creative process. Here, Pariser points to Dean Simonton’s research on spontaneous creativity and the likelihood that similar creative ideas will pop up in a lot of different places the same time. “A perfectly relevant environment,” laments Pariser, “lacks the ability to recognize this kind of variation.”

Pariser suggests that this narrowing of information doesn’t need to be inevitable. “We need media systems to make us uncomfortable. We need media to help us pay attention to the things that we don’t know. We need systems that don’t block us.”

In the meantime, he’s starting to work on a site he’s going to call thingsyoullhate.com. It’s not up yet, but when it is, Pariser wants it to highlight ideas and things that people completely unlike you think are great.